Process and equipment for pickling a metal strip

ABSTRACT

Process and equipment for pickling a metal strip, in particular a rolled strip, by means of a pickling plant, through which the metal strip passes and in which the metal strip is pickled using a pickling liquid, the pickling result being a function of pickling parameters. The pickling result is measured and at least one pickling parameter is automatically varied, as a function of the measurement of the pickling result, so as to improve the pickling result.

FIELD OF THE INVENTION

The present invention relates to a process and to equipment for picklinga metal strip, in particular a rolled strip, by means of a picklingplant, through which the metal strip passes and in which the metal stripis pickled using a pickling liquid, the pickling result being a functionof pickling parameters.

BACKGROUND INFORMATION

In order to clean metal strips, in particular in order to remove scalelayers on rolled strips, the metal strips are pickled in a picklingplant using a pickling liquid, generally acid. The amount removed by thepickling is a function of pickling parameters. These are, for example:temperature of the pickling liquid, speed at which the metal strippasses through the pickling plant, the acid content in the picklingliquid, the metal content in the pickling liquid, in particular the ironcontent in the pickling liquid, strip parameters, such as material andgeometric dimensions, and the turbulent pressure of the pickling liquid.These pickling parameters have to be set in such a way that as far aspossible, the desired amount of material is removed from the metalstrip. Deviations from the desired optimum value are associated withhigh costs. If too much material is removed, i.e., if it is not only thescale layer that is removed from a rolled strip but also metal from thesurface of the rolled strip, then the metal or iron content in thepickling liquid is increased to a disproportionate extent. Since thepurification of the pickling liquid is complicated and expensive, toohigh a removal rate is undesirable. In addition, in the event of toohigh a removal rate, damage to the metal strip may occur. On the otherhand, if too much material, in particular too much scale, remains on themetal strip, then this has to pass through the pickling plant again.This additional operation is complicated and expensive.

Setting the pickling parameters to achieve the best possible picklingresult is conventionally carried out, by an operator of the picklingplant. However, this leads to fluctuations in the pickling result. Thepickling result is to be understood, for example, as the amount ofmaterial removed or the amount of scale that has remained on the metalstrip.

SUMMARY

An object of the present invention is to provide a process and equipmentfor pickling a metal strip by means of which the pickling result isimproved. Furthermore, it is desirable to reduce the costs for thepickling of a metal strip.

According to the present invention, the pickling result is measured andat least one pickling parameter is automatically varied, as a functionof the measurement of the pickling result, so as to improve the picklingresult. The automatic variation allows the setting of the correspondingpickling parameter by an operator to be dispensed with. In this way, amore constant and better pickling result is achieved. A saving is alsomade in corresponding operating personnel. The pickling parameters to beset include, for example, the temperature of the pickling liquid in thepickling plant, which is determined, for example, from the temperatureof the pickling liquid in the feed into the pickling plant and thetemperature of the pickling liquid in the discharge from the picklingplant, the speed of the metal strip, the acid parameters of the picklingliquid, the iron concentration in the pickling liquid, the turbulentpressure of the pickling liquid in the pickling plant and the propertiesof the metal strip, such as its material and its geometric dimensions.In this case, the temperature of the pickling liquid is the picklingparameter that is particularly suitable for automatic setting. Since thetemperature of the pickling liquid in the pickling plant is difficult tomeasure and difficult to control, use is advantageously made of the feedtemperature of the pickling liquid into the pickling plant, thedischarge temperature of the pickling liquid from the pickling plant orboth temperatures instead of the temperature of the pickling liquid inthe pickling plant.

The pickling result is advantageously measured by measuring defectsand/or unpickled points on the metal strip. The defects and/or unpickledpoints are advantageously classified and counted. The classification ofthe defects and/or unpickled points is in this case advantageouslycarried out in relation to their size and/or their shape. The defectsand/or unpickled points classified and counted in this way areadvantageously evaluated. The evaluation is carried out using a fuzzyevaluator, a neural network or evaluator a neural fuzzy assessor.However, the measured values can also be evaluated directly, that is tosay unclassified, by a fuzzy evaluated, a neural network or a neuralfuzzy evaluated, but indirect evaluation, that is to say the evaluationof the classified and counted defects and/or unpickled points, is moreadvantageous. The result of the evaluation using a fuzzy evaluator, aneural network or a neural fuzzy evaluator are set points for at leastone pickling parameter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a strip plant operated in accordance with the presentinvention.

FIG. 2 shows an arrangement for training an assessor.

DETAILED DESCRIPTION

In FIG. 1, reference symbol 1 designates a pickling plant, through whicha metal strip 2 passes in the direction of the arrow designated byreference symbol 3. The metal strip 2 is pickled in the pickling plant1, using a pickling liquid. The pickling liquid is fed to the picklingplant 1 from a pickling liquid tank 13 via feed lines 18, 19 and a heatexchanger 10. For the purpose of pickling, the pickling liquid issprayed against the metal strip 2 from nozzles 6, 7. The pickling liquidrunning away is intercepted and fed to the pickling liquid tank 13 via aline 20.

The heat exchanger 10 is used for heating the pickling liquid. For thispurpose, steam from a steam generator 12 is fed to the heat exchanger 10via a steam line 16. The amount of steam can be set via a valve 11. Thesteam condenses in the heat exchanger 10. The water thus produced is fedto the steam generator 12 via a condensate line 17.

The pickling result, i.e. the amount of material removed, or the amountof undesired material, such as scale, for example, that has remained onthe metal strip 2, is a function of pickling parameters. These picklingparameters may be, for example, the temperature of the pickling liquidin the pickling plant 1, the speed v of the metal strip 1, the acidparameters c_(s) of the pickling liquid, the iron concentration c_(Fe)in the pickling liquid, the turbulent pressure p of the pickling liquidin the pickling plant 1 and the properties B of the metal strip, such asits material and its geometric dimensions. In the present exemplaryembodiment, the temperature of the pickling liquid is the only picklingparameter influenced. This is a particularly advantageous configuration,but the pickling result is improved further if further picklingparameters are set in a similar fashion.

The temperature T_(Z) of the pickling liquid in the feed and thetemperature T_(A) of the pickling liquid in the discharge are measuredusing temperature measuring instruments 9 and 8.

The pickling result is measured by means of an optical measuringinstrument 4. The signal from the measuring instrument 4 is fed to aclassifier 5, in which defects on the metal strip 2 or unpickled pointsof a material to be pickled away, such as scale, for example, areclassified and counted. The defects or points of unremoved material maybe classified, for example, in accordance with the defect categories“hole”, “dart spot”, “light spot”, “long dark stripes”, “long brightstripes”, “short dark stripes” and “short light stripes”, in accordancewith the following table:

Definition as a function of the speed Defect v = 360 v = 600 v = 1400categories m/min m/min m/min v = any Hole Ø ≧ 0.25 Ø ≧ 0.3 Ø > 0.75 — mmmm mm Dark spot Ø ≧ 0.85 Ø ≧ 1.0 Ø > 1.75 — mm mm mm Light spot Ø ≧ 0.85Ø ≧ 1.0 Ø > 1.75 — mm mm mm Long dark Width Width Width ≧ 0.25 mmstripes ≧0.25 mm ≧0.25 mm ≧0.25 mm (low Length Length Length contrast) ≧ 3 m  ≧ 5 m ≧10 m Long bright Width Width Width ≧ 0.25 mm stripes≧0.25 mm ≧ 0.25 mm ≧0.25 mm (low Length Length Length contrast)  ≧ 3 m ≧ 5 m ≧10 m Short dark Width Width Width — stripes ≧ 0.25 mm ≧ 0.25 mm≧ 0.25 mm (high Length Length Length contrast) ≧ 15 m ≧ 20 m ≧30 m ShortWidth Width Width — bright ≧ 0.25 mm ≧ 0.25 mm ≧ 0.25 mm stripes LengthLength Length (high ≧ 15 m ≧ 20 m ≧30 m contrast)

The frequencies of the individual defect categories are fed to anevaluator 15. This ascertains a set point T_(Z) ^(★) for the temperatureof the pickling liquid in the feed from the frequencies of the defectcategories, from the temperature T_(A) of the pickling liquid in thedischarge, the temperature T_(Z) of the pickling liquid in the feed, thespeed v of the metal strip 2, the acid parameters c_(s) of the picklingliquid, the iron concentration c_(Fe) in the pickling liquid, theturbulent pressure p of the pickling liquid and the properties B of themetal strip 2.

The evaluator 15 is advantageously designed as a fuzzy evaluator, as aneural network or as a neural fuzzy evaluator. In this case, the neuralfuzzy evaluator considered is advantageously a neural fuzzy systemaccording to the article “Neuro-Fuzzy”, H.-P. Preuβ, V. Tresp,VDI-Berichte 113, ISBN 3-18-091113-1, 1994, pages 89 to 122.

The set points T_(Z) ^(★) for the temperature of the pickling liquid inthe feed are fed to a controller 14, which sets the valve 11 as afunction of the temperature T_(Z) of the pickling liquid in the feed andthe set point T_(Z) ^(★) of the temperature of the pickling liquid inthe feed.

FIG. 2 shows equipment similar to that in FIG. 1. However, a set pointT_(Z)★^(B) is predefined for the controller 14 by an operator 21. Theset point T_(Z)★ of the temperature of the pickling liquid in the feed,which is ascertained by the evaluator 15, does not go into thecontroller 14. The equipment according to FIG. 2 has a learningalgorithm 23, by means of which the evaluator 15 ascertains a set pointfor the temperature in the feed as a function of the set point T_(Z)★ ofthe temperature of the pickling liquid in the feed, which is ascertainedby the evaluator 15, of the set point T_(Z)★^(B) of the temperature ofthe pickling liquid in the feed, which is ascertained by the operator21, and as a function of further pickling parameters: temperature T_(Z)of the pickling liquid in the feed, temperature T_(A) of the picklingliquid in the discharge, the speed v of the metal strip 2, the acidparameters C_(s) of the pickling liquid, the iron concentration c_(Fe)in the pickling liquid, the turbulent pressure p of the pickling liquidin the pickling plant and the properties B of the metal strip 2.

What is claimed is:
 1. A method for pickling a metal strip, comprisingpassing the metal strip through a pickling plant whereby the metal stripis pickled by a pickling liquid; measuring a pickling result, thepickling result being a function of pickling parameters and measured bymeasuring defects including unpickled points on the metal strip; andautomatically changing at least one of the pickling parameters toimprove the pickling results.
 2. The method according to claim 1,wherein the at least one of the pickling parameters includes atemperature of the pickling liquid in a pickling liquid feed, atemperature of the pickling liquid in a pickling liquid discharge, aspeed of the metal strip, acid parameters of the pickling liquid, ironconcentration in the pickling liquid, and a turbulent pressure of thepickling liquid.
 3. The method according to claim 1, wherein the atleast one of the pickling parameters changed is a temperature of thepickling liquid.
 4. The method according to claim 1, wherein the defectson the metal are classified and counted.
 5. The method according toclaim 1, wherein the defects on the metal strip are classified andcounted in relation to size and shape of each defect.
 6. The methodaccording to claim 1, wherein the at least one of the picklingparameters is automatically changed using one of: i) a fuzzy evaluator,ii) a neural network, and iii) a neural fuzzy evaluator, the at leastone of the pickling parameters being automatically changed as a functionof defects on the metal strip to improve the pickling result, thedefects including unpickled points of a material to be pickled off themetal strip.
 7. The method according to claim 6, wherein one of theneural network and the neural fuzzy evaluator is used for automaticallychanging the at least one of the pickling parameters, the method furthercomprising: setting by an operator of the pickling plant the picklingparameters; comparing the pickling parameters set by the operator topickling parameters determined by the one of the neural network and theneural fuzzy evaluator; and training the one of the neural network andthe neural fuzzy evaluator to reduce a deviation between the picklingparameters set by the operator and the pickling parameters determined bythe one of the neural network and the neural fuzzy evaluator.
 8. Themethod according to claim 1, wherein the at least one of the picklingparameters is automatically changed using one of: i) a fuzzy evaluator,ii) a neural network, and iii) a neural fuzzy evaluator, and at leastone of pickling parameters being automatically changed as a function ofa classification of defects on the metal strip to improve the picklingresult, the defects including unpickled points of a material to bepickled off the metal strip.
 9. A system for pickling a metal strip,comprising a pickling plant through which the metal strip passes andwhereby the metal strip is pickled by a pickling liquid; an instrumentfor measuring defects on the metal strip including unpickled points; anda means for classifying and counting said defects.
 10. A systemaccording to claim 9 further comprising an evaluator selected from thegroup consisting of a fuzzy evaluator, a neural network, and a neuralfuzzy evaluator.